System and method for detecting a problem tooth

ABSTRACT

A system and method for visualizing a dental image that includes a plurality of high resolution dental data, a plurality of tooth objects, at least one threshold and a processing module is described. The plurality of high resolution dental data that is generated using computed tomography. The plurality of tooth objects selected for each tooth from the dental data includes at least one of an enamel object, a dentin object, a pulp object, a root object, and a nerve object. The at least one threshold is used to detect at least one problem tooth. The processing module detects at least one problem tooth. Additionally, the processing module mathematically models the growth of at least one tooth object, the decay for at least one tooth object, or the combination thereof. Furthermore, the processing module determines the effect the problem tooth has on at least one other tooth object.

CROSS-REFERENCE

This patent application is a continuation of patent application Ser. No.11/890,533 filed on Aug. 6, 2007, which claims the benefit ofprovisional patent application 60/837,311, filed Aug. 11, 2006, all ofwhich are incorporated herein by reference in their entirety.

FIELD

The present invention relates to a system and method for detecting aproblem tooth. More specifically, the present invention is related tomathematically modeling the growth of at least one tooth object, thedecay for at least one tooth object, or the combination thereof.

BACKGROUND

Generally, dental images are displayed in two-dimensions using lighttables, e.g. X-rays. These two dimensional views provide a singleperspective of the image. Three-dimensional (3-D) imaging systems havealso been developed. These systems provide high-definition digitalimaging with relatively short scan times, e.g. 20 seconds. The imagereconstruction takes less than two minutes. The X-ray source istypically a high frequency source with a cone x-ray beam, and employs animage detector with an amorphous silicon flat panel. The images are12-bit gray scale and may have a voxel size of 0.4 mm to 0.1 mm. Imageacquisition is performed in a single session and is based on a 360degree rotation of the X-ray source. The output data are digital imagesthat are stored using conventional imaging formats such as the DigitalImaging and Communications in Medicine (DICOM) standard.

The 3-D volumetric imaging system provides complete views of oral andmaxillofacial structures. The volumetric images provide complete 3-Dviews of anatomy for a more thorough analysis of bone structure andtooth orientation. These 3-D images are frequently used for implant andoral surgery, orthodontics, and TMJ analysis. There are a variety ofdifferent software solutions that can be integrated into the 3-D dentalimaging systems. These third party solutions are generally related toimplant planning, and assist in planning and placement of the implants.Additionally, the 3-D dental images can be used for developing models toassist in planning an operation.

In spite of the advances in the 3-D imaging systems and the 3-D imagingsoftware, the software techniques for visualization of the dental imagesdo not provide a dentist with sufficient flexibility to manipulate the3-D image. Additionally, the visualization features provided by currentthird party solutions lack the ability to detect objects, detectirregularities, and detect anomalies.

SUMMARY

A system and method for visualizing a dental image that includes aplurality of high resolution dental data, a plurality of tooth objects,at least one threshold and a processing module is described. Theplurality of high resolution dental data is generated using computedtomography. The plurality of tooth objects selected for each tooth fromthe dental data includes at least one of an enamel object, a dentinobject, a pulp object, a root object, and a nerve object. The at leastone threshold is used to detect at least one problem tooth. Theprocessing module detects at least one problem tooth. Additionally, theprocessing module mathematically models the growth of at least one toothobject, the decay for at least one tooth object, or the combinationthereof. Furthermore, the processing module determines the effect theproblem tooth has on at least one other tooth object.

In one illustrative embodiment, the system and method includes adatabase that further includes a plurality of data fields that include aplurality of standard shapes associated with each tooth and a pluralityof bone density data for each section of tooth. Additionally, thedatabase includes a plurality of normative standards and at least onestatistical standard for anomaly detection.

In another illustrative embodiment, the system and method includesidentifying a common boundary between at least two tooth objects.

In yet another illustrative embodiment, the system and method includesidentifying a particular tooth for further analysis. Also, the systemand method includes analyzing the tooth objects for the particulartooth. Furthermore, the system and method includes analyzing theparticular tooth object by slicing the tooth object at one or morelocations.

FIGURES

Embodiments for the following description are shown in the followingdrawings:

FIG. 1A is shows an illustrative system overview.

FIG. 1B is an illustrative general purpose computer.

FIG. 1C is an illustrative client-server system.

FIG. 2 is an illustrative raw image.

FIG. 3 is an illustrative object identification flowchart.

FIG. 4A is an illustrative drawing showing jaw object identification.

FIG. 4B is an illustrative drawing showing tooth object identification.

FIG. 5 is an illustrative 3-D image of a tooth object.

FIG. 6 is an illustrative first slice of the tooth object in FIG. 5.

FIG. 7 is an illustrative second slice of the tooth object in FIG. 5.

FIG. 8 is an illustrative third slice of the tooth object in FIG. 5.

FIG. 9 is an illustrative flowchart for anomaly detection and formodeling growth rates.

FIGS. 10A and 10B shows a normal orientation for a wisdom tooth.

FIGS. 11A and 11B shows the beginning phase of horizontal impaction.

FIGS. 12A and 12B shows an illustrative example of cyst formation.

FIGS. 13A and 13B shows an illustrative example of cyst growth.

FIGS. 14A and 14B shows the resulting tooth decay and continuing cystgrowth.

DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof, and in which is shownby way of illustration specific embodiments in which the invention maybe practiced. These embodiments are described in sufficient detail toenable those skilled in the art to practice the invention, and it is tobe understood that other embodiments may be utilized and thatstructural, logical and electrical changes may be made without departingfrom the spirit and scope of the claims. The following detaileddescription is, therefore, not to be taken in a limited sense.

Note, the leading digit(s) of the reference numbers in the Figurescorrespond to the figure number, with the exception that identicalcomponents which appear in multiple figures are identified by the samereference numbers.

The systems and methods described herein are generally related tovisualization tools that operate with 3-D images generated usingtomography. Tomography is imaging by sections or sectioning. Themathematical procedures for imaging are referred to as tomographicreconstruction. Imaging is the process of creating a virtual image of aphysical object, its detailed structure, its substructure or anycombination thereof. Those skilled in the art shall appreciate thattomographic imaging includes analyzing the attenuation of the capturedimage using the Radon transform and filtered back projection. There area variety of different types of tomography including but not limited toAtom Probe Tomography, Computed Tomography, Electrical ImpedanceTomography, Magnetic Resonance Tomography, Optical Coherence Tomography,Positron Emission Tomography, Quantum Tomography, Single Photon EmissionComputed Tomography, and X-Ray Tomography. Attenuation refers to anyreduction in signal strength.

The systems and methods described herein allow improved visualization,object identification, anomaly detection, and predictive growth ratefeatures. Visualization refers to the process of taking one or moreimages and incorporates a comprehension of the physical relationship orsignificance of the features contained in the images. An object is aphysical relationship within an image that is capable of being graspedthrough visualization and an object is comprised of a plurality ofvoxels that presume a common basis. A variety of techniques, methods,algorithms, mathematical formulae, or any combination thereof may beused to identify a common basis. In the illustrative examples, elementssuch as location, bone density, shape or a combination thereof may beused to identify at least one common basis that is used for objectidentification. Bone density is the measure of mass of bone in relationto volume. Therefore, one or more common basis may be used for objectidentification.

It shall be appreciated by those of ordinary skill in the art that thesystems and methods described herein can be applied to a plurality ofdifferent modalities. A modality in a medical image is any of thevarious types of equipment or probes used to acquire images of the body.Magnetic Resonance Imaging is an example of a modality in this context.

Referring to FIG. 1A there is shown an illustrative system. Theillustrative system 200 receives a 3-D dental image 202 that is storedin a first database 204 that stores archived images. A digitalacquisition and processing component 208 processes received 3-D dentalimages. Particular information that is used to process the 3-D dentalimages is stored in the second database 206. An interactive graphicaluser interface 210 permits a user to manipulate the processed images andto interact with each illustrative dental object. By way of example andnot of limitations, the 3-D dental image is generated by a medicalimaging device such as an i-CAT 3-D Imaging System from Imaging SciencesInternational.

The databases 204 and 206 comprise a plurality of data fields including,but not limited to, data fields that correspond to the location for aplurality of teeth, a plurality of locations for each section of tooth,a plurality of standard shapes associated with each tooth, a pluralityof standard shapes associated with each of the sections of tooth, and aplurality of bone density data for each section of tooth.

The digital processing component 208 is configured to process the 3-Dimage, and is in operative communication with the database. The digitalprocessing component is configured to provide improved visualization ofthe medical image. The digital processing component 208 is configured toidentify an object by combining a plurality of voxels having a commondensity and tagging the object using the methods described herein. Avoxel is a volume element that represents a value in 3-D space. Commondensity is a density associated with a particular object in an image, inwhich a degree of attenuation within the image is associated withdensity.

Additionally, the digital processing component 208 is also configured topermit modifying the shape of at least one object. Furthermore, thedigital processing component 208 is configured to provide a method fordetecting anomalies and mathematically modeling growth rates.

In one embodiment the digital processing component 208 is a computerhaving a processor as shown in FIG. 1B. The illustrative general purposecomputer 10 is suitable for implementing the systems and methodsdescribed herein. The general purpose computer 10 includes at least onecentral processing unit (CPU) 12, a display such as monitor 14, and aninput device 15 such as cursor control device 16 or keyboard 17. Thecursor control device 16 can be implemented as a mouse, a joy stick, aseries of buttons, or any other input device which allows user tocontrol the position of a cursor or pointer on the display monitor 14.Another illustrative input device is the keyboard 17. The generalpurpose computer may also include random access memory {RAM) 18, harddrive storage 20, read-only memory (ROM) 22, a modem 26 and a graphicco-processor 28. All of the elements of the general purpose computer 10may be tied together by a common bus 30 for transporting data betweenthe various elements.

The bus 30 typically includes data, address, and control signals.Although the general purpose computer 10 illustrated in FIG. 1B includesa single data bus 30 which ties together all of the elements of thegeneral purpose computer 10, there is no requirement that there be asingle communication bus which connects the various elements of thegeneral purpose computer 10. For example, the CPU 12, RAM 18, ROM 22,and graphics co-processor might be tied together with a data bus whilethe hard disk 20, modem 26, keyboard 24, display monitor 14, and cursorcontrol device are connected together with a second data bus (notshown). In this case, the first data bus 30 and the second data buscould be linked by a bi-directional bus interface (not shown).Alternatively, some of the elements, such as the CPU 12 and the graphicscoprocessor 28 could be connected to both the first data bus 30 and thesecond data bus and communication between the first and second data buswould occur through the CPU 12 and the graphics co-processor 28. Themethods of the present invention are thus executable on any generalpurpose computing architecture, but there is no limitation that thisarchitecture is the only one which can execute the methods of thepresent invention.

Various visualization and analysis application may be run on theillustrative general purpose computer 10. For example, BioImage andBioPSE Power App is a visualization and analysis application developedby the University of Utah that may run on the computer 10. The softwareprograms explore scalar data sets such as medical imaging volumes. Inoperation, the user chooses an input data set. BioImage supports avariety of different industry standard formats including DICOM andAnalyze. For example, a dental data set containing a single tooth may beloaded into these programs.

After the data is loaded, the illustrative software program permits theuser to resample, crop, histogram or median filter the data. Using acropping filter permits visually removing the excess data from theborders of the volume. The GUI permits the user to explore the datavolume in both 2-D and 3-D using the rendering panes in the software.

The software also permits slice views wherein the user can change slicesand can adjust the contrast and brightness of the data. Yet anotherfeature of BioImage is the volume rendering engine. From the volumerendering tab, the user turns on the direct volume renderingvisualization. The volume rendering algorithm uses a transfer functionto assign color and opacity based on both data values and gradientmagnitudes of the volume. Thus, the interface between the dentin andpulp of the tooth may be colored differently. The dentin is a calcifiedtissue of the body, and along with enamel, cementum and pulp are thefour major components of teeth. Pulp is the part in the center of atooth make up of living soft tissue and cells called odontoblasts.

Alternatively, the methods described herein may use a client/serverarchitecture which is shown in FIG. 1C. It shall be appreciated by thoseof ordinary skill in the art that the client/server architecture 50 canbe configured to perform similar functions as those performed by thegeneral purpose computer 10. In the client-server architecturecommunication generally takes the form of a request message 52 from aclient 54 to the server 56 asking for the server 56 to perform a serverprocess 58. The server 56 performs the server process 58 and sends backa reply 60 to a client process 62 resident within client 54. Additionalbenefits from use of a client/server architecture include the ability tostore and share gathered information and to collectively analyzegathered information. In another alternative embodiment, a peer-to-peernetwork (not shown) can used to implement the methods described herein.

In operation, the general purpose computer I 0, client/server networksystem 50, or peer-to-peer network system execute a sequence ofmachine-readable instructions. These machine readable instructions mayreside in various types of signal bearing media. In this respect, oneaspect of the present invention concerns a programmed product,comprising signal-bearing media tangibly embodying a program ofmachine-readable instructions executable by a digital data processorsuch as the CPU 12 for the general purpose computer 10.

It shall be appreciated by those of ordinary skill that the computerreadable medium may comprise, for example, RAM 18 contained within thegeneral purpose computer 10 or within a server 56. Alternatively, thecomputer readable medium may be contained in another signal-bearingmedia, such as a magnetic data storage diskette that is directlyaccessible by the general purpose computer 10 or the server 56. Whethercontained in the general purpose computer or in the server, the machinereadable instructions within the computer readable medium may be storedin a variety of machine readable data storage media, such as aconventional “hard drive” or a RAID array, magnetic tape, electronicread-only memory (ROM), an optical storage device such as CD-ROM, DVD,or other suitable signal bearing media including transmission media suchas digital and analog and communication links. In an illustrativeembodiment, the machine-readable instructions may comprise softwareobject code from a programming language such as C++, Java, or Python.

Referring to FIG. 2 there is shown an illustrative raw image of a mouthand a tooth. In general, FIG. 2 provides a visual aid of the basicanatomy of the mouth and the tooth similar to what may be generatedusing the illustrative i-CAT imaging system described above. This visualaid has many limitations, namely, the multiple objects in the image havenot been identified. Additionally, the image is essentially a raw imagethat has not been standardized using some type of calibrated sample.

Referring to FIG. 3 there is shown an illustrative flowchart of a methodfor visualizing objects in a 3-D image 70. The illustrative method 70for visualizing a 3-D medical image comprises receiving a plurality ofhigh resolution 3-D medical data at block 72 that are generated using atomography technique, e.g. computed tomography (CT) scans. By way ofexample and not of limitation, the method for visualizing a dental imagecomprises receiving a plurality of high resolution 3-D dental dataassociated with a patient's mouth that is generated using x-raytomography.

The high resolution 3-D data received at block 72 includes a standardfor calibration purposes. For example, with respect to CT scans, thestandard may have a particular density that can be associated with abone density. The standard is composed of a material that can beassociated with the bone density of an illustrative tooth. The standardmay placed adjacent to the patient and held physically or mechanicallyin place. Alternatively, the patient may place the standard in the mouthand bite the standard.

The method then proceeds to block 74 where the high resolution data isconverted into an image comprised of a plurality of cubic voxels. Atblock 76, the method proceeds to identify the location for each cubicvoxel. The method then proceeds to identify a degree of attenuation forthe voxels at block 78. Attenuation is the reduction in amplitude andintensity of a signal.

At block 80, the method associates a common density with the degree ofattenuation. The method also associates the degree of attenuation forthe standard with the previously determined standard density related toblock 72. For example, the degree of attenuation for each voxel isassociated with at least one of a plurality of common bone densities.

The method then proceeds to block 82 that identifies an object bycombining the voxels having the common density and determines an initialshape for the object. By way of example and not of limitation, theidentifying of the object may comprise comparing the initial shape ofthe object to a standard shape. The common density may also be modifiedby a user, thereby resulting in the object having a different shape. Forexample, the 3-D data may be dental data and the common density is abone density associated with teeth, mouth, or jaw. By way of example andnot of limitation, the method then proceeds to generate dental objectsby combining voxels having one of the common bone densities. Theboundaries for each dental object are also determined. Variousopportunities may be presented where each dental object is compared to astandard shape to confirm identification of each dental object.

The method then proceeds to block 84 where at least one object is thentagged for further analysis 78. For example, the tagged objects in thetooth may be associated with a “metatag” so that each object can bequickly identified and viewed. A “metatag” as used herein refers to a“tag” that is associated with each object, wherein the “tag” issearchable and is used to provide a structured means for identifyingobject so that the “tagged” object or objects can be viewed. The taggedobject may then be extracted for further analysis. The extracted objectmay then be viewed using a plurality of different perspectives. Thetagged and extracted object can then be viewed by slicing the object atdesired locations. An illustrative object may be a particular toothobject, an enamel object, a dentin object, a pulp object, a root object,a nerve object, or any other such dental object associated with mouthand jaw. The plurality of objects may also be identified such as teethobjects, a plurality of nerve objects, and a plurality of bone objects.More generally, a plurality of objects may also be identified bycombining the voxels having one of a plurality of different commondensities and by determining the boundaries for each object.

At block 86, each of the objects is then compared to a standard shape,and each of the objects is then tagged to permit one or more objects tobe combined. The plurality of objects may also be identified such asteeth objects, a plurality of nerve objects, a plurality of boneobjects, or any other such object.

The flowchart also describes modifying the shape of at least one objectin a scanned 3-D medical image at block 88. After the method proceeds totag a first object and a second object, a common boundary between thefirst object and the second object is identified. The common boundary isconfigured to identify a change in bone density between the first objectand the second object. The method permits a user to modify the commonboundary by permitting the user to modify the apparent bone density ofthe first object. The method also provides for coloring each voxelaccording to each of the bone densities.

The common boundary spans a relatively broad area when there is littlechange in bone density between the first object and the second object.The method also permits evaluating a plurality of standard shapes whengenerating the first object and the second object. For example, each ofthe plurality of objects may have a plurality of tags, in which each tagmay be extracted from the image as represented by block 90. By way ofexample and not of limitation, the first object is tagged as a firsttooth and the second object is a second tooth. In another illustrativeexample, the first object and said second object is selected from agroup consisting of a tooth object, an enamel object, a dentin object, apulp object, a root object, a nerve object, a plurality of teethobjects, a plurality of nerve objects, or a plurality of bone objects.Additionally, the method 70 also supports performing imaging operationsuch as slicing objects as represented by block 92 and described infurther detail below.

In operation, the method involves known physiologies discovered by themethod above and supports analyzing relevant materials. After a 3-DDICOM file is converted to 3-D volumetric image, if the process has notalready been completed, the volume is oriented according to axes, bodyportion contained, scale, etc. Object identification may be performed asa function of common densities, density transitions, and known orstandard shape similarities. A map of the objects can then be createdand displayed.

The systems and method described may be applied to non-specific objects,issue identification by density, adjacent material, and generallocation. Margin (junction) shape determination, specific material shape(object) determination based on material profile, and cataloging of samemay also be performed. For example, the identification of objects,passageways, etc. (e.g. teeth, nerve canals, implants, vertebrae, jaw,etc.) is performed. The objective is to identify recurring examples ofsimilar objects such as teeth, and to catalog their identification, bothby normative standards and by reference to statistically compiledidentifiers and shape.

Referring to FIG. 4A there is shown an illustrative drawing 100 withdental and jaw object identification. As presented, the tagged objectsin the tooth are associated with a “metatag” or “searchable tag” so thateach object can be quickly identified and viewed. A variety of softtissues objects such as nerve objects are shown. The nerve objects referto sensitive tissue in the pulp of a tooth, or any bundle of nervefibers running to various organs in the body. Additionally, a standard102 for calibration purposes is shown. By way of example and not oflimitation, these nerve objects are typically identified using MRI or CTscans.

Referring to FIG. 4B there is shown an exploded view of a third molartooth object 110, which is identified using the systems and methodsdescribed herein. The tooth is a set of hard, bone-like structuresrooted in sockets in the jaws of vertebrates, typically composed of acore of soft pulp surrounded by a layer of hard dentin that is coatedwith cementum or enamel at the crown and used for biting or chewingfoods or as a means of attack or defense. The tooth object is composedof a variety of different objects. One such object is an enamel objectwhich is the hard, calcareous substance covering the exposed portion ofa tooth. Another object is dentin, which is the main, calcareous part ofa tooth, beneath the enamel, and surrounding the pulp chamber and rootcanals. The pulp object is the soft tissue forming the inner structureof a tooth and containing nerves and blood vessels. The root object isthe embedded part of an organ or structure such as a tooth, or nerve,and includes the part of the tooth that is embedded in the jaw andserves as support. The root canal objects refers to the portion of thepulp cavity inside the root of the tooth, namely, the chamber within theroot of the tooth that contains the pulp. The Gingiva or Gum object isthe firm connective tissue covered by mucous membrane that envelops thealveolar arches of the jaw and surrounds the neck of the teeth. The neckobject is the constriction between the root and the crown and can alsobe referred to as the Cemental-Enamel-Junction.

Referring to FIG. 5 there is shown an illustrative 3-D image of anillustrative tooth object 120. The tooth object 120 comprises each ofthe objects described above such as the enamel, dentin and pulp. Avariety of different slices of the 3-D image are presented. For exampleFIG. 6 provides an illustrative first slice 122 of the tooth object inFIG. 5. FIG. 7 provides an illustrative second slice 124 of the toothobject in FIG. 5, and FIG. 8 is an illustrative third slice 126 of thetooth object. Each of these drawings depict that the tagged objects canbe “sliced” to provide a clearer view of the particular tooth. Thisslicing process may also be used for anomaly detection as describedbelow.

Referring to FIG. 9 there is shown an illustrative flowchart for anomalydetection and for modeling growth rates that is a continuation of theflowchart in FIG. 3. An anomaly is a deviation or departure from anormal, or common order, or form, or rule, and is generally used torefer to a substantial defect. An “irregularity” is distinguishable froman anomaly since “irregular” simply means lacking symmetry, evenness, orhaving a minor defect. The flowchart describes a method for identifyinganomalies in a scanned 3-D dental image. The method accesses a database206 (shown in FIG. IA) having a plurality of data fields related tolocation for a plurality of teeth, a plurality of locations for eachsection of tooth, a plurality of standard shapes associated with theteeth, a plurality of standard shapes associated with each of thesections of tooth, and a plurality of bone density data for each sectionof tooth.

As previously described in FIG. 3, a 3-D image having a plurality ofcubic voxels is generated, and the location for each voxel isidentified. For the illustrative example described herein, the methodthen proceeds to identify a signal strength for each cubic voxel, andassociates the signal strength for each voxel with the bone densitydata. Signal strength refers to the total amount of power of RF receivedby the receiver. This is divided into useful signal, referred to asEC/IO, and the noise floor.

The method at block 92 performs object identification at block 132 wherean illustrative first object is generated by combining a first groupingof voxels having a first bone density. At block 134, the illustrativefirst object is compared to objects in the database 206 (shown in FIG.IA). The method then proceeds to identify irregularities at block 136.At block 138, anomalies are identified after comparing the first objectto one or more fields in the database 206. The database comprises aplurality of normative standards and statistical standards for anomalydetection that distinguished between anomalies and irregularities.

The anomaly detection at block 138 may also comprise generating aplurality of other objects and tagging the objects so that one or moreobjects may be combined. The method may then proceed to identify one ormore anomalies associated with the plurality of objects. For example,the method supports identifying one or more anomalies associated with atleast one object that is tagged as a tooth object, in which the toothobject further comprises a plurality of tagged objects selected from agroup consisting of an enamel object, a dentin object, a pulp object, aroot object, and a nerve object.

The method then proceeds to block 140 and performs the process ofmathematically modeling growth rates. Although the illustrative exampleof teeth is described herein, teeth are not the only objects that growand it shall be appreciated that the systems and methods describedherein may be used to model bone growth and bone decay in general.Growth rate projections may be based on such parameters as age, gender,height, weight, ethnicity, and other such parameters that may bevaluable to mathematically modeling growth rates. Those skilled in theart shall appreciate that measurements such as bone growth are alsoprimary indicators and are provided for illustrative purposes only.

At block 142, the relational effects resulting from having modeled thegrowth of a particular object are determined. Thus, the modeled growthresults in changes to the local conditions, and these changes arepresented to the user.

The method then proceeds to decision diamond 144 where the determinationof whether to change any of the parameters described above is necessary.Therefore, modeled growth rates may be changed, thresholds for anomalydetection may be changed, and the basis for object identification mayalso be modified.

In operation, at least one expected growth rate is provided for at leastone tooth. After the first tooth object is compared to the first toothobject data in the database, the method then proceeds to mathematicallymodel a growth rate for the first tooth object using the expected growthrate, and modifies the location of a plurality of objects surroundingthe first tooth object due to the growth of the first tooth object. Themethod may then proceed to identify an anomaly after comparing the firstobject to one or more fields in the database. Objects may then be taggedso that so that one or more objects may be combined. Anomalies may thenbe associated with one or more tooth objects selected from a groupconsisting of an enamel object, a dentin object, a pulp object, a rootobject, and a nerve object.

By way of example and not of limitation, anomaly detection may beperformed by identifying at least one threshold for anomaly detection.The gathered data is then compared to the threshold to determine if oneor more anomalies have been detected.

The potential anomaly may also be associated with a first mathematicalmodel, which is then compared to a second “normative” mathematical modelusing recently extracted data. The first mathematical model may havevariables that can be modified, which mirrors the ability to modify theobject. The correlation between the first mathematical model and secondmathematical model is determined by a correlation estimate that may bebased on the concordances of randomly sampled pairs.

Additionally, the method may also provide for the use of clusteringanalysis. Clustering provides an additional method for analyzing thedata. Spatial cluster detection has two objectives, namely, to identifythe locations, shapes and sizes of potentially anomalous spatialregions, and to determine whether each of these potential clusters ismore likely to a valid cluster or simply a chance cluster. The processof spatial cluster detection can separated into two parts: first,determining the expected result, secondly, determining which regionsdeviate from the expected result.

The process of determining which regions deviate from the expectedresult can be performed using a variety of techniques. For example,simple statistics can be used to determine a number of spatial standarddeviations, and anomalies simply fall outside the standard deviations.Alternatively, spatial scan statistics can be used as described byKulldorff. (M. Kulldorff. A Spatial Scan Statistic. Communications inStatistics: Theory and Methods 26(6), 1481-1496, 1997.) In this method,a given set of spatial regions are searched and regions are found usinghypothesis testing. A generalized spatial scan framework can also beused. (M. R. Sabhnani, D. B. Neill, A. W. Moore, F.-C. tsui, M. M.Wagner, and J. U. Espino. Detecting anomalous patterns in pharmacyretail data. KDD Workshop on Data Mining Methods for Anomaly Detection,2005.)

It shall be appreciated by those skilled in the art that the particularalgorithm that is used for anomaly detection will depend on theparticular application and be subject to system limitations. Thus, avariety of different algorithms for anomaly detection may be used.

An illustrative method for anomaly detection for a tooth object is shownin FIG. 10 through FIG. 14. The anomaly detection also includes modelingthe crown of a tooth object and the effect the tooth object has onsurrounding objects. Referring to FIG. 10A and the exploded view in FIG.10B, there is shown a normal orientation for a wisdom tooth. In thisorientation, the wisdom tooth in question has sufficient space so thatthere will be no horizontal impaction.

With respect to another patient, an anomaly 150 is detected in FIG. 11Aand the exploded view in FIG. 11B. The anomaly reflects that this is aproblem tooth, and this anomaly can immediately be brought to thephysician's attention using the systems and method described herein.This image also shows the beginning phase of horizontal impaction, andthe formation of a cyst. A cyst is an abnormal membranous sac containinga gaseous, liquid, or semisolid substance. Additionally, at locationthere is shown a dental cavity/caries that are just starting.

Referring to FIG. 12A and the exploded view in FIG. 12B there is shownan illustrative example of the progression, i.e. growth, of the cyst andcavity/caries after the appropriate growth models have been associatedwith the particular tooth 150 and the surrounding teeth. At FIG. 13A andthe exploded view in 13B, there is shown the effect of cyst growth, andthe initial stages of tooth decay on tooth 150. Tooth decay is aninfectious, transmissible, disease caused by bacteria. The damage doneto teeth by this disease is commonly known as cavities. Tooth decay cancause pain and lead to infections in surrounding tissues and tooth lossif not treated properly. The progression of the tooth decay and cystformation is then shown in FIG. 14A and exploded view in FIG. 14B.

The illustrative systems and methods described above have been developedto assist in visualizing objects, permitting a user to modify objects,anomaly detection for objects, and for modeling growth rates associatedwith particular objects. It shall be appreciated by those of ordinaryskill in the various arts having the benefit of this disclosure that thesystem and methods described can be applied to many disciplines outsideof the field of dentistry. Furthermore, alternate embodiments of theinvention which implement the systems in hardware, firmware, or acombination of hardware and software, as well as distributing themodules or the data in a different fashion will be apparent to thoseskilled in the art. Further still, the illustrative methods describedmay vary as to order and implemented algorithms.

Although the description above contains many limitations in thespecification, these should not be construed as limiting the scope ofthe claims but as merely providing illustrations of some of thepresently preferred embodiments of this invention. Many otherembodiments will be apparent to those of skill in the art upon reviewingthe description. Thus, the scope of the invention should be determinedby the appended claims, along with the full scope of equivalents towhich such claims are entitled.

What is claimed is:
 1. A system for visualizing a dental image,comprising: a plurality of high resolution dental data generated usingcomputed tomography; a plurality of tooth objects selected for eachtooth from the dental data from the group consisting of an enamelobject, a dentin object, a pulp object, a root object, and a nerveobject; at least one threshold to detect at least one problem tooth; aprocessing module that detects at least one problem tooth; theprocessing module mathematically models the growth of at least one toothobject; and the processing module determines the effect the problemtooth has on at least one other tooth object.
 2. The system of claim 1further comprising a database that includes a database that furtherincludes a plurality of data fields that include a plurality of standardshapes associated with each tooth and a plurality of bone density datafor each section of tooth.
 3. The system of claim 2 wherein the databaseincludes a plurality of normative standards and at least one statisticalstandard for anomaly detection.
 4. The system of claim 2 furthercomprising a common boundary between at least two tooth objects that isidentified by the system.
 5. The system of claim 2 further comprising aparticular tooth that is identified by the system for further analysis.6. The system of claim 5 wherein the processing module analyzes thetooth objects for the particular tooth.
 7. The system of claim 6 whereinthe processing module analyzes the particular tooth object by slicingthe tooth object at one or more locations.
 8. A system for visualizing adental image, comprising: a plurality of high resolution dental datagenerated using computed tomography; a plurality of tooth objectsselected for each tooth from the dental data from the group consistingof an enamel object, a dentin object, a pulp object, a root object, anda nerve object; at least one threshold to detect at least one problemtooth; a processing module that detects at least one problem tooth; theprocessing module mathematically models the decay for at least one toothobject; and the processing module determines the effect the problemtooth has on at least one other tooth object.
 9. The system of claim 8further comprising a database that includes a database that furtherincludes a plurality of data fields that include a plurality of standardshapes associated with each tooth and a plurality of bone density datafor each section of tooth.
 10. The system of claim 9 wherein thedatabase includes a plurality of normative standards and at least onestatistical standard for anomaly detection.
 11. The system of claim 9further comprising a common boundary between at least two tooth objectsthat is identified by the system.
 12. The system of claim 9 furthercomprising a particular tooth that is identified by the system forfurther analysis.
 13. The system of claim 5 wherein the processingmodule analyzes the tooth objects for the particular tooth and analyzesthe particular tooth object by slicing the tooth object at one or morelocations.
 14. A method for visualizing a dental image, comprising:receiving a plurality of high resolution dental data that is generatedusing computed tomography; identifying a plurality of tooth objects foreach tooth from the dental data, wherein the plurality of tooth objectsis selected from the group consisting of an enamel object, a dentinobject, a pulp object, a root object, and a nerve object; providing atleast one threshold to detect at least one problem tooth; detecting theat least one problem tooth; mathematically modeling at least one of thegrowth or decay of the at least one tooth object; and determining theeffect the problem tooth has on at least one other tooth object.
 15. Themethod of claim 14 further comprising providing a database that includesa database that further includes a plurality of data fields that includea plurality of standard shapes associated with each tooth and aplurality of bone density data for each section of tooth.
 16. The methodof claim 15 wherein the database includes a plurality of normativestandards and at least one statistical standard for anomaly detection.17. The method of claim 15 further comprising identifying a commonboundary between at least two tooth objects.
 18. The method of claim 15further comprising identifying a particular tooth for further analysis.19. The method of claim 18 further comprising analyzing the toothobjects for the particular tooth.
 20. The method of claim 19 furthercomprising analyzing the particular tooth object by slicing the toothobject at one or more locations.